Image classification is a fundamental issue in computer vision and machine learning area. With advances in graphical processing unit technology, it has been observed that image classifications with deep Neural Networks (NNs) give more accurate results compared to traditional methods [1],[2]. In deep NNs, input datasets are processed in deep convolutional layers, which can learn feature representations hierarchically, beginning from low level to more abstract representation. Especially, Convolutional NNs (CNNs) were proposed because they can preserve spatial relationships between image pixels [3]–​[5].

Very deep CNNs have been usually preferred because deeper NNs have more representational power [6]. Deeper networks get this power from shallower feature representations, which are composed hierarchically, into deeper representations. However, when these networks begin to converge, a degradation problem occurs. Because, when the number of layers are increased, accuracy gets saturated and then degrades rapidly. In other words, adding more layers to a deep NN increases training error. Therefore, training of very deep CNNs is hard due to vanishing gradients in the long forward feed and backward propagate process [7],[8].

Recently, Residual NETwork (ResNET) has been applied in order to overcome vanishing gradient problems in CNNs [9],[10]. A ResNET has shortcut connections parallel to the normal convolutional layers. Those shortcuts act like highways and the gradients can easily flow back. The clearest advantage of ResNETs is their fast training and convergence [9],[11].

Residual blocks and chosen activation functions have a major role in the success of training of NNs. However, their effect in deep NNs is still unclear for image classification. Therefore, in this work, four network models (Table I) have been implemented to analyze the effect of residual blocks and two activation functions, which are Rectified Linear Unit (ReLU) and Scaled Exponential Linear Unit (SELU) [12].

TABLE 1. Network Models Implemented in This Work

Those network models designed without residual blocks (2nd and 4th model) correspond to plain networks. Therefore, in this study, performance evaluations have been performed for two plain networks and two residual connection based networks.

In this work, to evaluate accuracies of these network models in image classification, they have been applied to solve a challenging issue, which is automated classification of skin diseases from colored digital images. For this purpose, the following common skin diseases have been handled: Acne (Fig. 1.a,b), Rosacea (Fig. 1.c) Hemangioma (Fig. 1.d), Psoriasis (Fig. 1.e), Seborrheic Dermatitis (Fig. 1.f).

All networks have been designed with 18 layers and 100 epochs. The learning rate is 0.00001. Also, ADAptive Moment (ADAM) estimation that is an efficient stochastic optimization has been used [13],[14].

Fig. 1.

Acne (a,b) [15], Rosacea (c) [16], Hemangioma (d) [17], Psoriasis (e) [18], Seborrheic Dermatitis (f) [19]

Show All

Remaining sections have been organized as follows: In section 2, a short information about ResNET architecture is given for those who are unfamiliar with ResNet. In Section 3, experimental results obtained from the network models are presented in terms of loss, validation loss, accuracy and validation accuracy. In section 4, conclusions are explained.

ResNETs consist of many residual blocks. A residual block is formulated by:

View Source\begin{gather*} {Y_l} = h({X_l}) + F({X_l},{{\text{W}}_1})\tag{1} \\ {X_{l + 1}} = f({Y_1})\tag{2}\end{gather*}

In (1), the input feature of the residual block l is represented by X_l. F refers to a residual function. The term W_l, which is computed by Wl={Wl,k|l≤k≤K} (K: number of layers in the residual block), corresponds to weights of the residual block l. The term f is a function, which is applied after element-wise addition. In the original ResNET [9], f is ReLU. Identity mapping is provided using the h function by h(X_l) = X_l. The equation given in (2) can be set into (1) to obtain the following equation, in case of the function f is an identity mapping (X_{l +1} ≡ Y_l);

View Source\begin{equation*}{X_{l + 1}} = {X_l} + F({X_l},{W_l})\tag{3}\end{equation*}

Therefore, the next iterative statement can be written as;

View Source\begin{equation*}{X_{l{\text{ + 2}}}}{\text{ = }}{X_{l{\text{ + 1}}}}{\text{ + }}F\left( {{X_{l{\text{ + 1}}}}{\text{,}}{W_{l{\text{ + 1}}}}} \right){\text{ = }}{X_l} + F\left( {{X_l},{W_l}} \right) + F\left( {{X_{l + 1}},{W_{l + 1}}} \right)\tag{4}\end{equation*}

Then, the following equation is obtained;

View Source\begin{equation*}{X_L} = {X_l} + \sum\limits_{i = l}^{L - 1} {F\left( {{X_i},{W_i}} \right)} \tag{4}\end{equation*}

where L refers to a deeper block and l refers to a shallower block. Fig. 2 shows a residual block [10].

B. 2nd Model: Network with ReLU, Batch Normalization, without Residual Block

The 2nd model has been designed without using residual blocks to see their effects. This network model has been constructed with ReLU activation function and batch normalization.

Fig. 2.

Residual Block

Show All

C. 3rd Model: Network with SELU, Residual Block, without Batch Normalization

Residual blocks and SELU activation function, which was used in [22] firstly, has been applied in this model. Similar to ReLU, SELU can overcome the vanishing gradient problem in NNs. Also, in some cases, it can provide better performance compared to ReLU [23]. SELU is formulated by:

f(x)=λ{xαeα−αifx≥0ifx<0(5)

View Source\begin{equation*} f\left( x \right) = \lambda \begin{cases} x&{ifx \geq 0} \\ {\alpha {e^\alpha } - \alpha }&{ifx < 0} \end{cases} \tag{5}\end{equation*}

(with α ≈1.7 and λ ≈1 ) and does not have batch normalization layers [20].

These four network models have been applied with 100 colored digital images showing skin diseases. Loss and accuracy values have been obtained from these models to evaluate their image classification performance for five common skin diseases.

In this work, balanced number of images has been used for those five classes. 70% of these total images was selected randomly for training, 15% of the total images was used for validation and the remaining 15% of the total images was used for testing.

Fig. 3 shows loss and validation loss values obtained by the 1st and 2nd model. These models have been designed with ReLU activation function. Differentiation between loss values of these two models shows the effect of the residual block used in the 1st model.

Fig. 3.

Loss and validation loss scores computed by the 1st model (ReLU, batch normalization and residual block) and 2nd model (ReLU and batch normalization)

Show All

Fig. 4 shows loss and validation loss values obtained by the 3rd and 4th model. These models have been designed with SELU activation function. Differentiation between loss values of these two models shows the effect of the residual block used in the 3rd model.

Fig. 4.

Loss and validation loss scores computed by the 3rd model (SELU and residual block) and 4th model (SELU)

Show All

Comparative results of the loss values obtained from the 1st and 3rd model are presented in Fig. 5 to indicate the effect of ReLU and SELU activation functions.

Loss and validation loss scores computed by each model can be seen in Fig. 6.

In addition to these loss values, the results indicating accuracy of the classification have been obtained. Fig. 7 shows the accuracy and validation accuracy values obtained by the 1st and 2nd model to examine the effect of residual block used in the 1st model.

Fig. 5.

Loss and validation loss values scores computed the 1st model (ReLU, batch normalization and residual block) and 3rd model (SELU and residual block)

Show All

Fig. 6.

Loss and validation loss scores computed by each model

Show All

Fig. 7.

Accuracy and validation accuracy values obtained by the 1st model (ReLU, batch normalization and residual block) and 2nd model (ReLU and batch normalization)

Show All

The performance in terms of accuracy of the models with residual blocks and SELU activation function (3rd and 4th model) is presented in Fig. 8.

Fig. 8.

Accuracy and validation accuracy values obtained by the 3rd model (SELU and residual block) and 4th model (SELU)

Show All

The results obtained by the accuracy values from the 1st and 3rd model are given in Fig. 9 to indicate the effect of ReLU and SELU activation functions.

Fig. 9.

Accuracy and validation accuracy values obtained by the 1st model (ReLU, batch normalization, and residual block) and 3rd model (SELU and residual block)

Show All

Fig. 10 shows the accuracy and validation accuracy values obtained by each model for comparative evaluations.

The results in terms of validation loss and accuracy for each model are given as a list in Table II. It has been observed that the highest (lowest) validation accuracy is 97.01% (95.57%).

Fig. 10.

Accuracy and validation accuracy values of all models

Show All

TABLE II. Validation Loss and Accuracy of Each Models

In this work, four network models have been examined to see effects of two activation functions (ReLU and SELU) and residual blocks for image classification. Comparative analyses of these models have been performed with the results obtained by skin disease classification from colored images.

Elimination of vanishing gradient problem and self normalization with the residual blocks provides stability in training of network models (Fig. 7, Fig. 8).

The network models designed using SELU activation function have similar fluctuations according to the accuracy values (Fig. 8).

The network model designed with SELU and without residual block gives the highest validation accuracy. On the other hand, the lowest validation loss is obtained by the network model designed with RELU and residual block (Fig. 6, Fig. 10).

Experimental results show that automated classification of five skin diseases can be performed with high accuracy using deep networks. These models will be tested with increased number and variation of images as an extension of this work.